Do not buy frontier tokens for routine inference
A practical routing pattern for keeping Codex, Claude, and other frontier models on high-value work while cheaper open-model lanes handle routine volume.
Audience
Founders and engineering leads
Metric
Frontier only for judgment
Sources
3 official links
The expensive mistake
Most AI bills get out of control when every task is sent to the most expensive model. Routine extraction, summarization, draft generation, test scaffolding, and repeated agent loops rarely need frontier reasoning on every request.
Price Gouge treats frontier models as a scarce judgment layer. They stay available for planning, architecture, difficult debugging, code review, research synthesis, and hard reasoning, but routine inference moves to cheaper open-model or batch lanes.
Default routing policy
Start with an open-model lane for routine work, add a batch lane for non-urgent backfills, and keep a small frontier escalation lane for low-confidence or high-risk outputs.
- Open-model lane: extraction, summaries, drafts, triage, repeated agent loops.
- Batch lane: documents, logs, offline analysis, retryable jobs.
- Frontier lane: architecture, reviews, final synthesis, risky edge cases.
What to measure
The right split depends on acceptance rate, cache hit rate, output length, retries, and latency. A low token price can still lose if the model causes more retries or longer outputs.
Run a small sample through at least two cheap providers and one frontier evaluator. Promote the cheapest lane that clears your quality bar, then cap the frontier escalation share before production rollout.